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Model uncertainties and EnOI in the IBI36/NEMO model

by ECOLA last modified Oct 31, 2014 03:52 PM

Quantifying uncertainties and EnOI data assimilation in a Bay of Biscay configuration of  the MERCATOR-Océan IBI36/NEMO system

G. Quattrocchi (post-doc), V. Vervatis (post-doc), P. De Mey, C-E. Testut (MERCATOR-Océan), N. Ayoub, J. Chanut (MERCATOR-Océan), Y. Drillet (MERCATOR-Océan), G. Reffray (MERCATOR-Océan)

Contact : P. De Mey, N. Ayoub

Characterisation of errors of a Bay of Biscay configuration of IBI36/NEMO in response to wind uncertainties (MyOcean project)

We carried out an ensemble experiment in a regional model of the Bay of Biscay, a regional zoom of the IBI configuration of the ocean model NEMO, by randomly perturbing winds (Quattrocchi et al., 2014). 

The figure below shows the kurtosis (order 4 moment) in excess of the Gaussian value.  In general, the shelves appear to be more peaked than normal distribution, and the deep areas flatter.   Since many shelf areas are mixed in January, it is expected that wind uncertainties will not significantly impact SST uncertainties, whose distribution will then appear as “peaked”.  On the other hand, by carrying water around and contributing to mixing properties horizontally, advective processes which are active in particular in deeper and slope areas can consistently appear as “flattening” distributions.  Therefore data assimilation methods able to work with non-Gaussian dynamics have to be used in the regional/coastal ocean.  This is the case of Ensemble methods.

Article:

Quattrocchi, G., P. De Mey, N. Ayoub, V. Vervatis, C-E. Testut, G. Reffray, J. Chanut, and Y. Drillet, 2014: Characterization of errors of a regional model of the Bay of Biscay in response to wind uncertainties: a first step toward a data assimilation system suitable for coastal sea domains. Journal of Operational Oceanography, Volume 7, Number 2, August 2014, pp. 25-34(10).

Abstract:

This study is aimed at exploring the errors of a regional model of the Bay of Biscay, a regional zoom of the IBI configuration of the ocean model NEMO, with the ultimate objective of guiding the choice and implementation of a data assimilation system in that region. An ensemble experiment was carried out by randomly perturbing winds along a base of EOFs with the aim to mimic a potential source of error in the model forecasts. A characterisation was attempted with proxy forecast errors by using statistical moments of order 1 to 4. The temporal variability of model correction patterns in a hypothetical data assimilation system was also illustrated. Significant departures from linear/Gaussian response were found, as well as well-marked non-stationarities in the error patterns. Within the limits of the experimental protocol, this could be technically applicable to other coastal areas as the study illustrates the likely limits of stationary/Gaussian data assimilation approaches in the Bay of Biscay.

Towards a 4D EnOI setup in a Bay of Biscay configuration of IBI36/NEMO (MyOcean2 and MyOcean Follow-On projects)

In a test of the above ideas within the context of a data assimilation exercise in the Bay of Biscay NEMO system, predicted uncertainties are introduced by performing stochastic modelling of the wind forcing.  A twin experiment is carried out starting from the SAM2 Data Assimilation system used for operational purposes in Mercator-Ocean but introducing elements of an Ensemble Kalman Filter (EnKF).  Ensembles are used to update the background error covariances and are compared to operational results. The ensemble method is built upon a 4DEnOI kernel, whereas the operational SAM2 platform uses a fixed-basis SEEK method. In the twin experiment, observations are simulated from a member of the ensemble not used in the subsequent analyses, with the help of a rank histogram. Sensitivity experiments explore the relative performance of the 4DEnOI approach, in order to identify the pros and cons of implementing the method in future operational systems. The ensemble covariances seem to be able to extract information for most of the variables of the oceanic system. First results indicate that the ensemble method outperforms in many cases the current operational method in the chosen experimental protocol.

Article:

Vervatis, V., C.E. Testut, P. De Mey, N. Ayoub, J. Chanut, G. Quattrocchi, 2014: Data assimilative twin experiments in a regional configuration of the Bay of Biscay: a 4D EnOI approach based on stochastic modelling of the wind forcing and dynamic forecast error covariances. In preparation.

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